Basis of the Grad-CAM is a calculation of the gradient of output and designated convolutional layer.
For image classification problems, the model is generally consisted with convolutional layers and multi-layer perceptrons [1]}.
To obtain an intensity map of the interest, the gradient between the output value and last convolutional layer which corresponds to the layer just before the multi-layer perceptron, needs to be calculated.
The model used in the \(C_D\)  estimation of a cylinder wake has a similar structure to those image classification networks since the output is a scalar value as presented in figure REF \((a)\) .
Hence, we use the gradient \(\partial y/\partial A_{ij}^k\)  just as the classification problems, where \(y\)  denotes the estimated \(C_D\)  value and \(A_{ij}^k\)  explains the output of channel \(k\)  at the last convolutional layer, respectively.
The weight \(\alpha ^k\)  for each channel can be then obtained taking the average of the gradients for each value as,
\(\alpha ^k = \frac{1}{Z}\sum _i\sum _j\frac{\partial y}{\partial A^k_{ij}},\) 
